SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 4901–4950 of 6748 papers

TitleStatusHype
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
A Data-driven Market Simulator for Small Data Environments—0
A Data-Driven Method for Recognizing Automated Negotiation Strategies—0
A data filling methodology for time series based on CNN and (Bi)LSTM neural networks—0
A data imputation method for multivariate time series based on generative adversarial network—0
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
A Deep Learning Approach for COVID-19 Trend Prediction—0
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM—0
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles—0
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data—0
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems—0
A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines—0
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data—0
A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies—0
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services—0
A deep network approach to multitemporal cloud detection—0
A Deep Structural Model for Analyzing Correlated Multivariate Time Series—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
A detection analysis for temporal memory patterns at different time-scales—0
A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting—0
A Direct Estimation of High Dimensional Stationary Vector Autoregressions—0
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction—0
Admissible Time Series Motif Discovery with Missing Data—0
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders—0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
Advancing multivariate time series similarity assessment: an integrated computational approach—0
Adversarial attacks against Bayesian forecasting dynamic models—0
Adversarial Attacks on Multivariate Time Series—0
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces—0
Adversarially learned anomaly detection for time series data—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour—0
A dynamic conditional approach to portfolio weights forecasting—0
Aedes-AI: Neural Network Models of Mosquito Abundance—0
A fast algorithm for complex discord searches in time series: HOT SAX Time—0
A Fast Evidential Approach for Stock Forecasting—0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Affine and Regional Dynamic Time Warpng—0
A first econometric analysis of the CRIX family—0
A First Option Calibration of the GARCH Diffusion Model by a PDE Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified